Advances in Atmospheric Sciences最新文献

The downward shortwave radiation (DSR) is an important part of the Earth’s energy balance, driving Earth’s system’s energy, water, and carbon cycles. Due to the harsh Antarctic environment, the accuracy of DSR derived from satellite and reanalysis has not been systematically evaluated over the transect of Zhongshan station to Dome A, East Antarctica. Therefore, this study aims to evaluate DSR reanalysis products (ERA5-Land, ERA5, MERRA-2) and satellite products (CERES and ICDR) in this area. The results indicate that DSR exhibits obvious monthly and seasonal variations, with higher values in summer than in winter. The ERA5-Land (ICDR) DSR product demonstrated the highest (lowest) accuracy, as evidenced by a correlation coefficient of 0.988 (0.918), a root-mean-square error of 23.919 (69.383) W m⁻², a mean bias of −1.667 (−28.223) W m⁻² and a mean absolute error of 13.37 (58.99) W m⁻². The RMSE values for the ERA5-Land reanalysis product at seven stations, namely Zhongshan, Panda 100, Panda 300, Panda 400, Taishan, Panda 1100, and Kunlun, were 30.938, 29.447, 34.507, 29.110, 20.339, 17.267, and 14.700 W m⁻², respectively; with corresponding bias values of 9.887, −12.159, −19.181, −15.519, −8.118, 6.297, and 3.482 W m⁻². Regarding seasonality, ERA5-Land, ERA5, and MERRA-2 reanalysis products demonstrate higher accuracies during spring and summer, while ICDR products are least accurate in autumn. Cloud cover, water vapor, total ozone, and severe weather are the main factors affecting DSR. The error of DSR products is greatest in coastal areas (particularly at the Zhongshan station) and decreases towards the inland areas of Antarctica.

Using monthly observations and ensemble hindcasts of the Nanjing University of Information Science and Technology Climate Forecast System (NUIST-CFS1.0) for the period 1983–2020, this study investigates the forecast skill of marine heatwaves (MHWs) over the globe and the predictability sources of the MHWs over the tropical oceans. The MHW forecasts are demonstrated to be skillful on seasonal-annual time scales, particularly in tropical oceans. The forecast skill of the MHWs over the tropical Pacific Ocean (TPO) remains high at lead times of 1–24 months, indicating a forecast better than random chance for up to two years. The forecast skill is subject to the spring predictability barrier of El Niño-Southern Oscillation (ENSO). The forecast skills for the MHWs over the tropical Indian Ocean (TIO), tropical Atlantic Ocean (TAO), and tropical Northwest Pacific (NWP) are lower than that in the TPO. A reliable forecast at lead times of up to two years is shown over the TIO, while a shorter reliable forecast window (less than 17 months) occurs for the TAO and NWP. Additionally, the forecast skills for the TIO, TAO, and NWP are seasonally dependent. Higher skills for the TIO and TAO appear in boreal spring, while a greater skill for the NWP emerges in late summer-early autumn. Further analyses suggest that ENSO serves as a critical source of predictability for MHWs over the TIO and TAO in spring and MHWs over the NWP in summer.

In a convective scheme featuring a discretized cloud size density, the assumed lateral mixing rate is inversely proportional to the exponential coefficient of plume size. This follows a typical assumption of −1, but it has unveiled inherent uncertainties, especially for deep layer clouds. Addressing this knowledge gap, we conducted comprehensive large eddy simulations and comparative analyses focused on terrestrial regions. Our investigation revealed that cloud formation adheres to the tenets of Bernoulli trials, illustrating power-law scaling that remains consistent regardless of the inherent deep layer cloud attributes existing between cloud size and the number of clouds. This scaling paradigm encompasses liquid, ice, and mixed phases in deep layer clouds. The exponent characterizing the interplay between cloud scale and number in the deep layer cloud, specifically for liquid, ice, or mixed-phase clouds, resembles that of shallow convection, but converges closely to zero. This convergence signifies a propensity for diminished cloud numbers and sizes within deep layer clouds. Notably, the infusion of abundant moisture and the release of latent heat by condensation within the lower atmospheric strata make substantial contributions. However, this role in ice phase formation is limited. The emergence of liquid and ice phases in deep layer clouds is facilitated by the latent heat and influenced by the wind shear inherent in the middle levels. These interrelationships hold potential applications in formulating parameterizations and post-processing model outcomes.

The shrinking Arctic sea-ice area (SIA) in recent decades is a striking manifestation of the ongoing climate change. Variations of the Arctic sea ice have been continuously observed by satellites since 1979, relatively well monitored since the 1950s, but are highly uncertain in the earlier period due to a lack of observations. Several reconstructions of the historical gridded sea-ice concentration (SIC) data were recently presented based on synthesized regional sea-ice observations or by applying a hybrid model–empirical approach. Here, we present an SIC reconstruction for the period 1901–2019 based on established co-variability between SIC and surface air temperature, sea surface temperature, and sea level pressure patterns. The reconstructed sea-ice data for March and September are compared to the frequently used HadISST1.1 and SIBT1850 datasets. Our reconstruction shows a large decrease in SIA from the 1920 to 1940 concurrent with the Early 20th Century Warming event in the Arctic. Such a negative SIA anomaly is absent in HadISST1.1 data. The amplitude of the SIA anomaly reaches about 0.8 mln km² in March and 1.5 mln km² in September. The anomaly is about three times stronger than that in the SIBT1850 dataset. The larger decrease in SIA in September is largely due to the stronger SIC reduction in the western sector of the Arctic Ocean in the 70°–80°N latitudinal zone. Our reconstruction provides gridded monthly data that can be used as boundary conditions for atmospheric reanalyses and model experiments to study the Arctic climate for the first half of the 20th century.

The increasing concentration of atmospheric CO₂ since the Industrial Revolution has affected surface air temperature. However, the impact of the spatial distribution of atmospheric CO₂ concentration on surface air temperature biases remains highly unclear. By incorporating the spatial distribution of satellite-derived atmospheric CO₂ concentration in the Beijing Normal University Earth System Model, this study investigated the increase in surface air temperature since the Industrial Revolution in the Northern Hemisphere (NH) under historical conditions from 1976–2005. In comparison with the increase in surface temperature simulated using a uniform distribution of CO₂, simulation with a nonuniform distribution of CO₂ produced better agreement with the Climatic Research Unit (CRU) data in the NH under the historical condition relative to the baseline over the period 1901–30. Hemispheric June–July–August (JJA) surface air temperature increased by 1.28°C ± 0.29°C in simulations with a uniform distribution of CO₂, by 1.00°C ± 0.24°C in simulations with a non-uniform distribution of CO₂, and by 0.24°C in the CRU data. The decrease in downward shortwave radiation in the non-uniform CO₂ simulation was primarily attributable to reduced warming in Eurasia, combined with feedbacks resulting from increased leaf area index (LAI) and latent heat fluxes. These effects were more pronounced in the non-uniform CO₂ simulation compared to the uniform CO₂ simulation. Results indicate that consideration of the spatial distribution of CO₂ concentration can reduce the overestimated increase in surface air temperature simulated by Earth system models.

Due to various technical issues, existing numerical weather prediction (NWP) models often perform poorly at forecasting rainfall in the first several hours. To correct the bias of an NWP model and improve the accuracy of short-range precipitation forecasting, we propose a deep learning-based approach called UNetMask, which combines NWP forecasts with the output of a convolutional neural network called UNet. The UNetMask involves training the UNet on historical data from the NWP model and gridded rainfall observations for 6-hour precipitation forecasting. The overlap of the UNet output and the NWP forecasts at the same rainfall threshold yields a mask. The UNetMask blends the UNet output and the NWP forecasts by taking the maximum between them and passing through the mask, which provides the corrected 6-hour rainfall forecasts. We evaluated UNetMask on a test set and in real-time verification. The results showed that UNetMask outperforms the NWP model in 6-hour precipitation prediction by reducing the FAR and improving CSI scores. Sensitivity tests also showed that different small rainfall thresholds applied to the UNet and the NWP model have different effects on UNetMask’s forecast performance. This study shows that UNetMask is a promising approach for improving rainfall forecasting of NWP models.

Using surface and balloon-sounding measurements, satellite retrievals, and ERA5 reanalysis during 2011–20, this study compares the precipitation and related wind dynamics, moisture and heat features in different areas of the South China Sea (SCS) before and after SCS summer monsoon onset (SCSSMO). The rainy sea around Dongsha (hereafter simply referred to as Dongsha) near the north coast, and the rainless sea around Xisha (hereafter simply referred to as Xisha) in the western SCS, are selected as two typical research subregions. It is found that Dongsha, rather than Xisha, has an earlier and greater increase in precipitation after SCSSMO under the combined effect of strong low-level southwesterly winds, coastal terrain blocking and lifting, and northern cold air. When the 950-hPa southwesterly winds enhance and advance northward, accompanied by strengthened moisture flux, there is a strong convergence of wind and moisture in Dongsha due to a sudden deceleration and rear-end collision of wind by coastal terrain blocking. Moist and warm advection over Dongsha enhances early and deepens up to 200 hPa in association with the strengthened upward motion after SCSSMO, thereby providing ample moisture and heat to form strong precipitation. However, when the 950-hPa southwesterly winds weaken and retreat southward, Xisha is located in a wind-break area where strong convergence and upward motion centers move in. The vertical moistening and heating by advection in Xisha enhance later and appear far weaker compared to that in Dongsha, consistent with later and weaker precipitation.

Cold surges (CSs) often occur in the mid-latitude regions of the Northern Hemisphere and have enormous effects on socioeconomic development. We report that the occurrences of CSs and persistent CSs (PCSs) have rebounded since the 1990s, but the trends related to the frequencies of strong CSs (SCSs) and extreme CSs (ECSs) changed from increasing to decreasing after 2000. The highest-ranked model ensemble approach was used to project the occurrences of various CSs under the SSP1-2.6, SSP2-4.5, and SSP5-8.5 scenarios. The frequencies of the total CSs show overall decreasing trends. However, under the SSP1-2.6 scenario, slight increasing trends are noted for SCSs and ECSs in China. Atmospheric circulations that are characterized by an anomalous anticyclonic circulation with a significantly positive 500-hPa geopotential height (Z500) anomaly at high latitudes along with significant negative anomalies in China were favorable for cold air intrusions into China. In addition, the frequencies of all CS types under the SPP5-8.5 scenario greatly decreased in the long term (2071–2100), a finding which is thought to be related to negative SST anomalies in the central and western North Pacific, differences in sea level pressure (SLP) between high- and mid-latitude regions, and a weaker East Asian trough. In terms of ECSs, the decreasing trends observed during the historical period were maintained until 2024 under the SSP1-2.6 scenario. Compared to the SSP1-2.6 scenario, the Z500 pattern showed a trend of strengthened ridges over the Ural region and northern East Asia and weakened troughs over Siberia (60°–90°E) under the SSP2-4.5 and SSP5-8.5 scenarios, contributing to the shift to increasing trends of ECSs after 2014.

Employing the nonlinear local Lyapunov exponent (NLLE) technique, this study assesses the quantitative predictability limit of oceanic mesoscale eddy (OME) tracks utilizing three eddy datasets for both annual and seasonal means. Our findings reveal a discernible predictability limit of approximately 39 days for cyclonic eddies (CEs) and 44 days for anticyclonic eddies (AEs) within the South China Sea (SCS). The predictability limit is related to the OME properties and seasons. The long-lived, large-amplitude, and large-radius OMEs tend to have a higher predictability limit. The predictability limit of AE (CE) tracks is highest in autumn (winter) with 52 (53) days and lowest in spring (summer) with 40 (30) days. The spatial distribution of the predictability limit of OME tracks also has seasonal variations, further finding that the area of higher predictability limits often overlaps with periodic OMEs. Additionally, the predictability limit of periodic OME tracks is about 49 days for both CEs and AEs, which is 5–10 days higher than the mean values. Usually, in the SCS, OMEs characterized by high predictability limit values exhibit more extended and smoother trajectories and often move along the northern slope of the SCS.

The Tibetan Plateau (TP) region, also known as the “Asian water tower”, provides a vital water resource for downstream regions. Previous studies of water cycle changes over the TP have been conducted with climate models of coarse resolution in which deep convection must be parameterized. In this study, we present results from a first set of high-resolution climate change simulations that permit convection at approximately 3.3-km grid spacing, with a focus on the TP, using the Icosahedral Nonhydrostatic Weather and Climate Model (ICON). Two 12-year simulations were performed, consisting of a retrospective simulation (2008–20) with initial and boundary conditions from ERA5 reanalysis and a pseudo-global warming projection driven by modified reanalysis-derived initial and boundary conditions by adding the monthly CMIP6 ensemble-mean climate change under the SSP5-8.5 scenario. The retrospective simulation shows overall good performance in capturing the seasonal precipitation and surface air temperature. Over the central and eastern TP, the average biases in precipitation (temperature) are less than −0.34 mm d⁻¹ (−1.1°C) throughout the year. The simulated biases over the TP are height-dependent. Cold (wet) biases are found in summer (winter) above 5500 m. The future climate simulation suggests that the TP will be wetter and warmer under the SSP5-8.5 scenario. The general features of projected changes in ICON are comparable to the CMIP6 ensemble projection, but the added value from kilometer-scale modeling is evident in both precipitation and temperature projections over complex topographic regions. These ICON-downscaled climate change simulations provide a high-resolution dataset to the community for the study of regional climate changes and impacts over the TP.